Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations3395
Missing cells1065
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory636.7 KiB
Average record size in memory192.0 B

Variable types

Numeric10
Text2
Categorical12

Alerts

Fri is highly overall correlated with weekdayHigh correlation
Mon is highly overall correlated with weekdayHigh correlation
Sat is highly overall correlated with weekdayHigh correlation
Sun is highly overall correlated with weekdayHigh correlation
Thurs is highly overall correlated with weekdayHigh correlation
Tues is highly overall correlated with weekdayHigh correlation
Wed is highly overall correlated with weekdayHigh correlation
chargeTimeHrs is highly overall correlated with dollarsHigh correlation
distance is highly overall correlated with managerVehicle and 1 other fieldsHigh correlation
dollars is highly overall correlated with chargeTimeHrsHigh correlation
endTime is highly overall correlated with startTimeHigh correlation
facilityType is highly overall correlated with locationIdHigh correlation
locationId is highly overall correlated with facilityTypeHigh correlation
managerVehicle is highly overall correlated with distance and 1 other fieldsHigh correlation
reportedZip is highly overall correlated with distanceHigh correlation
startTime is highly overall correlated with endTimeHigh correlation
userId is highly overall correlated with managerVehicleHigh correlation
weekday is highly overall correlated with Fri and 6 other fieldsHigh correlation
Sat is highly imbalanced (86.8%) Imbalance
Sun is highly imbalanced (93.9%) Imbalance
distance has 1065 (31.4%) missing values Missing
sessionId has unique values Unique
kwhTotal has 55 (1.6%) zeros Zeros
dollars has 3016 (88.8%) zeros Zeros

Reproduction

Analysis started2025-02-22 17:53:15.327231
Analysis finished2025-02-22 17:53:34.420178
Duration19.09 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

sessionId
Real number (ℝ)

Unique 

Distinct3395
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5487001.2
Minimum1004821
Maximum9998981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.7 KiB
2025-02-22T17:53:34.592102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1004821
5-th percentile1488389.6
Q13234666
median5451498
Q37746643.5
95-th percentile9539390.6
Maximum9998981
Range8994160
Interquartile range (IQR)4511977.5

Descriptive statistics

Standard deviation2590657.3
Coefficient of variation (CV)0.47214447
Kurtosis-1.2095201
Mean5487001.2
Median Absolute Deviation (MAD)2255191
Skewness0.02142914
Sum1.8628369 × 1010
Variance6.7115052 × 1012
MonotonicityNot monotonic
2025-02-22T17:53:34.818054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1366563 1
 
< 0.1%
8945459 1
 
< 0.1%
4842356 1
 
< 0.1%
9634467 1
 
< 0.1%
4827913 1
 
< 0.1%
9762000 1
 
< 0.1%
1268330 1
 
< 0.1%
7656000 1
 
< 0.1%
9832956 1
 
< 0.1%
3485664 1
 
< 0.1%
Other values (3385) 3385
99.7%
ValueCountFrequency (%)
1004821 1
< 0.1%
1006672 1
< 0.1%
1013222 1
< 0.1%
1016799 1
< 0.1%
1021429 1
< 0.1%
1022066 1
< 0.1%
1041671 1
< 0.1%
1044216 1
< 0.1%
1046212 1
< 0.1%
1046325 1
< 0.1%
ValueCountFrequency (%)
9998981 1
< 0.1%
9998167 1
< 0.1%
9997392 1
< 0.1%
9994024 1
< 0.1%
9990811 1
< 0.1%
9984728 1
< 0.1%
9984432 1
< 0.1%
9982427 1
< 0.1%
9982088 1
< 0.1%
9979636 1
< 0.1%

kwhTotal
Real number (ℝ)

Zeros 

Distinct873
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8096289
Minimum0
Maximum23.68
Zeros55
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size26.7 KiB
2025-02-22T17:53:34.999764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.26
Q14.35
median6.23
Q36.83
95-th percentile9.529
Maximum23.68
Range23.68
Interquartile range (IQR)2.48

Descriptive statistics

Standard deviation2.8927272
Coefficient of variation (CV)0.49791946
Kurtosis6.9889565
Mean5.8096289
Median Absolute Deviation (MAD)0.79
Skewness1.6052988
Sum19723.69
Variance8.3678709
MonotonicityNot monotonic
2025-02-22T17:53:35.185959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
 
1.6%
6.73 35
 
1.0%
6.88 31
 
0.9%
6.84 30
 
0.9%
6.76 29
 
0.9%
6.93 29
 
0.9%
6.83 28
 
0.8%
6.92 28
 
0.8%
6.71 28
 
0.8%
6.78 27
 
0.8%
Other values (863) 3075
90.6%
ValueCountFrequency (%)
0 55
1.6%
0.01 1
 
< 0.1%
0.02 3
 
0.1%
0.03 3
 
0.1%
0.04 1
 
< 0.1%
0.05 2
 
0.1%
0.06 2
 
0.1%
0.08 1
 
< 0.1%
0.09 2
 
0.1%
0.1 2
 
0.1%
ValueCountFrequency (%)
23.68 1
< 0.1%
22.14 1
< 0.1%
22.07 1
< 0.1%
22.03 1
< 0.1%
21.2 1
< 0.1%
21.16 1
< 0.1%
21.14 1
< 0.1%
20.95 1
< 0.1%
20.92 1
< 0.1%
20.6 1
< 0.1%

dollars
Real number (ℝ)

High correlation  Zeros 

Distinct49
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11826804
Minimum0
Maximum7.5
Zeros3016
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size26.7 KiB
2025-02-22T17:53:35.615844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.58
Maximum7.5
Range7.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49256195
Coefficient of variation (CV)4.1647933
Kurtosis67.653082
Mean0.11826804
Median Absolute Deviation (MAD)0
Skewness7.2785048
Sum401.52
Variance0.24261727
MonotonicityNot monotonic
2025-02-22T17:53:35.817255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 3016
88.8%
0.5 195
 
5.7%
0.58 21
 
0.6%
0.75 18
 
0.5%
0.83 16
 
0.5%
0.67 12
 
0.4%
1.42 9
 
0.3%
1.58 8
 
0.2%
1 7
 
0.2%
1.17 7
 
0.2%
Other values (39) 86
 
2.5%
ValueCountFrequency (%)
0 3016
88.8%
0.5 195
 
5.7%
0.58 21
 
0.6%
0.67 12
 
0.4%
0.75 18
 
0.5%
0.83 16
 
0.5%
0.92 7
 
0.2%
1 7
 
0.2%
1.08 5
 
0.1%
1.17 7
 
0.2%
ValueCountFrequency (%)
7.5 1
< 0.1%
6.42 1
< 0.1%
5.75 2
0.1%
5.33 1
< 0.1%
5.17 1
< 0.1%
5 1
< 0.1%
4.83 2
0.1%
4.75 2
0.1%
4.67 1
< 0.1%
4.58 1
< 0.1%
Distinct3393
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
2025-02-22T17:53:36.299216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters64505
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3391 ?
Unique (%)99.9%

Sample

1st row0014-11-18 15:40:26
2nd row0014-11-19 17:40:26
3rd row0014-11-21 12:05:46
4th row0014-12-03 19:16:12
5th row0014-12-11 20:56:11
ValueCountFrequency (%)
0015-10-01 55
 
0.8%
0015-09-28 47
 
0.7%
0015-09-23 47
 
0.7%
0015-09-25 43
 
0.6%
0015-09-02 40
 
0.6%
0015-09-30 40
 
0.6%
0015-09-10 39
 
0.6%
0015-08-19 38
 
0.6%
0015-09-24 38
 
0.6%
0015-07-23 37
 
0.5%
Other values (3443) 6366
93.8%
2025-02-22T17:53:36.944937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13864
21.5%
1 10535
16.3%
- 6790
10.5%
: 6790
10.5%
5 6078
9.4%
2 3931
 
6.1%
3395
 
5.3%
3 2716
 
4.2%
4 2543
 
3.9%
9 2051
 
3.2%
Other values (3) 5812
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 13864
21.5%
1 10535
16.3%
- 6790
10.5%
: 6790
10.5%
5 6078
9.4%
2 3931
 
6.1%
3395
 
5.3%
3 2716
 
4.2%
4 2543
 
3.9%
9 2051
 
3.2%
Other values (3) 5812
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 13864
21.5%
1 10535
16.3%
- 6790
10.5%
: 6790
10.5%
5 6078
9.4%
2 3931
 
6.1%
3395
 
5.3%
3 2716
 
4.2%
4 2543
 
3.9%
9 2051
 
3.2%
Other values (3) 5812
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 13864
21.5%
1 10535
16.3%
- 6790
10.5%
: 6790
10.5%
5 6078
9.4%
2 3931
 
6.1%
3395
 
5.3%
3 2716
 
4.2%
4 2543
 
3.9%
9 2051
 
3.2%
Other values (3) 5812
9.0%

ended
Text

Distinct3355
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
2025-02-22T17:53:37.374667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters64505
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3318 ?
Unique (%)97.7%

Sample

1st row0014-11-18 17:11:04
2nd row0014-11-19 19:51:04
3rd row0014-11-21 16:46:04
4th row0014-12-03 21:02:18
5th row0014-12-11 21:14:06
ValueCountFrequency (%)
0015-10-01 55
 
0.8%
0015-09-23 47
 
0.7%
0015-09-28 47
 
0.7%
0015-09-25 43
 
0.6%
0015-09-30 41
 
0.6%
0015-09-02 40
 
0.6%
0015-09-10 39
 
0.6%
0015-08-19 38
 
0.6%
0015-07-23 38
 
0.6%
0015-09-24 38
 
0.6%
Other values (2456) 6364
93.7%
2025-02-22T17:53:37.980211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15843
24.6%
1 9637
14.9%
- 6790
10.5%
: 6790
10.5%
5 6197
 
9.6%
2 3762
 
5.8%
3395
 
5.3%
6 2313
 
3.6%
7 2042
 
3.2%
4 1987
 
3.1%
Other values (3) 5749
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 64505
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 15843
24.6%
1 9637
14.9%
- 6790
10.5%
: 6790
10.5%
5 6197
 
9.6%
2 3762
 
5.8%
3395
 
5.3%
6 2313
 
3.6%
7 2042
 
3.2%
4 1987
 
3.1%
Other values (3) 5749
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 64505
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 15843
24.6%
1 9637
14.9%
- 6790
10.5%
: 6790
10.5%
5 6197
 
9.6%
2 3762
 
5.8%
3395
 
5.3%
6 2313
 
3.6%
7 2042
 
3.2%
4 1987
 
3.1%
Other values (3) 5749
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 64505
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 15843
24.6%
1 9637
14.9%
- 6790
10.5%
: 6790
10.5%
5 6197
 
9.6%
2 3762
 
5.8%
3395
 
5.3%
6 2313
 
3.6%
7 2042
 
3.2%
4 1987
 
3.1%
Other values (3) 5749
 
8.9%

startTime
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.743446
Minimum0
Maximum23
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.7 KiB
2025-02-22T17:53:38.198525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q111
median13
Q317
95-th percentile19
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2043697
Coefficient of variation (CV)0.2331562
Kurtosis-0.49958819
Mean13.743446
Median Absolute Deviation (MAD)3
Skewness0.020202239
Sum46659
Variance10.267985
MonotonicityNot monotonic
2025-02-22T17:53:38.503224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
11 504
14.8%
12 475
14.0%
17 437
12.9%
16 404
11.9%
10 295
8.7%
13 291
8.6%
18 233
6.9%
15 192
 
5.7%
9 158
 
4.7%
14 139
 
4.1%
Other values (13) 267
7.9%
ValueCountFrequency (%)
0 3
 
0.1%
1 3
 
0.1%
3 2
 
0.1%
4 4
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 53
 
1.6%
9 158
4.7%
10 295
8.7%
ValueCountFrequency (%)
23 2
 
0.1%
22 7
 
0.2%
21 14
 
0.4%
20 56
 
1.6%
19 119
 
3.5%
18 233
6.9%
17 437
12.9%
16 404
11.9%
15 192
5.7%
14 139
 
4.1%

endTime
Real number (ℝ)

High correlation 

Distinct24
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.455965
Minimum0
Maximum23
Zeros7
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size26.7 KiB
2025-02-22T17:53:38.724125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q114
median16
Q320
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4067322
Coefficient of variation (CV)0.20702112
Kurtosis1.1257537
Mean16.455965
Median Absolute Deviation (MAD)3
Skewness-0.52889405
Sum55868
Variance11.605824
MonotonicityNot monotonic
2025-02-22T17:53:38.891491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
15 554
16.3%
20 423
12.5%
21 357
10.5%
16 320
9.4%
14 308
9.1%
19 262
7.7%
13 261
7.7%
18 219
 
6.5%
17 197
 
5.8%
12 191
 
5.6%
Other values (14) 303
8.9%
ValueCountFrequency (%)
0 7
0.2%
1 7
0.2%
2 3
0.1%
3 2
 
0.1%
4 2
 
0.1%
5 1
 
< 0.1%
6 4
0.1%
7 1
 
< 0.1%
8 2
 
0.1%
9 3
0.1%
ValueCountFrequency (%)
23 15
 
0.4%
22 88
 
2.6%
21 357
10.5%
20 423
12.5%
19 262
7.7%
18 219
 
6.5%
17 197
 
5.8%
16 320
9.4%
15 554
16.3%
14 308
9.1%

chargeTimeHrs
Real number (ℝ)

High correlation 

Distinct3006
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8414876
Minimum0.0125
Maximum55.238056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.7 KiB
2025-02-22T17:53:39.049739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0125
5-th percentile0.81261111
Q12.1102778
median2.8088889
Q33.5441667
95-th percentile4.6111944
Maximum55.238056
Range55.225556
Interquartile range (IQR)1.4338889

Descriptive statistics

Standard deviation1.5074719
Coefficient of variation (CV)0.53052207
Kurtosis430.56107
Mean2.8414876
Median Absolute Deviation (MAD)0.71583333
Skewness12.72604
Sum9646.8506
Variance2.2724716
MonotonicityNot monotonic
2025-02-22T17:53:39.236414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.605 4
 
0.1%
0.025833333 4
 
0.1%
3.9375 4
 
0.1%
2.7475 3
 
0.1%
2.531944444 3
 
0.1%
2.506388889 3
 
0.1%
2.443611111 3
 
0.1%
3.940555556 3
 
0.1%
2.526111111 3
 
0.1%
3.667777778 3
 
0.1%
Other values (2996) 3362
99.0%
ValueCountFrequency (%)
0.0125 1
< 0.1%
0.012777778 1
< 0.1%
0.013333333 1
< 0.1%
0.014722222 1
< 0.1%
0.015 1
< 0.1%
0.016111111 1
< 0.1%
0.016666667 1
< 0.1%
0.0175 2
0.1%
0.018055556 1
< 0.1%
0.018611111 1
< 0.1%
ValueCountFrequency (%)
55.23805556 1
< 0.1%
11.58694444 1
< 0.1%
10.48833333 1
< 0.1%
9.836944444 1
< 0.1%
9.320277778 1
< 0.1%
9.241111111 1
< 0.1%
9.006111111 1
< 0.1%
8.8725 1
< 0.1%
8.845555556 1
< 0.1%
8.831388889 1
< 0.1%

weekday
Categorical

High correlation 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
Thu
735 
Wed
713 
Tue
635 
Mon
616 
Fri
610 
Other values (2)
86 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10185
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTue
2nd rowWed
3rd rowFri
4th rowWed
5th rowThu

Common Values

ValueCountFrequency (%)
Thu 735
21.6%
Wed 713
21.0%
Tue 635
18.7%
Mon 616
18.1%
Fri 610
18.0%
Sat 62
 
1.8%
Sun 24
 
0.7%

Length

2025-02-22T17:53:39.417532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T17:53:39.552136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
thu 735
21.6%
wed 713
21.0%
tue 635
18.7%
mon 616
18.1%
fri 610
18.0%
sat 62
 
1.8%
sun 24
 
0.7%

Most occurring characters

ValueCountFrequency (%)
u 1394
13.7%
T 1370
13.5%
e 1348
13.2%
h 735
7.2%
W 713
7.0%
d 713
7.0%
n 640
6.3%
M 616
6.0%
o 616
6.0%
F 610
6.0%
Other values (5) 1430
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 1394
13.7%
T 1370
13.5%
e 1348
13.2%
h 735
7.2%
W 713
7.0%
d 713
7.0%
n 640
6.3%
M 616
6.0%
o 616
6.0%
F 610
6.0%
Other values (5) 1430
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 1394
13.7%
T 1370
13.5%
e 1348
13.2%
h 735
7.2%
W 713
7.0%
d 713
7.0%
n 640
6.3%
M 616
6.0%
o 616
6.0%
F 610
6.0%
Other values (5) 1430
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 1394
13.7%
T 1370
13.5%
e 1348
13.2%
h 735
7.2%
W 713
7.0%
d 713
7.0%
n 640
6.3%
M 616
6.0%
o 616
6.0%
F 610
6.0%
Other values (5) 1430
14.0%

platform
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
ios
2234 
android
1155 
web
 
6

Length

Max length7
Median length3
Mean length4.3608247
Min length3

Characters and Unicode

Total characters14805
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowandroid
2nd rowandroid
3rd rowandroid
4th rowandroid
5th rowandroid

Common Values

ValueCountFrequency (%)
ios 2234
65.8%
android 1155
34.0%
web 6
 
0.2%

Length

2025-02-22T17:53:39.722067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T17:53:39.820785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ios 2234
65.8%
android 1155
34.0%
web 6
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 3389
22.9%
o 3389
22.9%
d 2310
15.6%
s 2234
15.1%
a 1155
 
7.8%
n 1155
 
7.8%
r 1155
 
7.8%
w 6
 
< 0.1%
e 6
 
< 0.1%
b 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 3389
22.9%
o 3389
22.9%
d 2310
15.6%
s 2234
15.1%
a 1155
 
7.8%
n 1155
 
7.8%
r 1155
 
7.8%
w 6
 
< 0.1%
e 6
 
< 0.1%
b 6
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 3389
22.9%
o 3389
22.9%
d 2310
15.6%
s 2234
15.1%
a 1155
 
7.8%
n 1155
 
7.8%
r 1155
 
7.8%
w 6
 
< 0.1%
e 6
 
< 0.1%
b 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 3389
22.9%
o 3389
22.9%
d 2310
15.6%
s 2234
15.1%
a 1155
 
7.8%
n 1155
 
7.8%
r 1155
 
7.8%
w 6
 
< 0.1%
e 6
 
< 0.1%
b 6
 
< 0.1%

distance
Real number (ℝ)

High correlation  Missing 

Distinct97
Distinct (%)4.2%
Missing1065
Missing (%)31.4%
Infinite0
Infinite (%)0.0%
Mean18.652378
Minimum0.8569106
Maximum43.059292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.7 KiB
2025-02-22T17:53:39.984782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.8569106
5-th percentile1.3751582
Q15.135871
median21.023826
Q327.285053
95-th percentile33.045027
Maximum43.059292
Range42.202381
Interquartile range (IQR)22.149182

Descriptive statistics

Standard deviation11.420571
Coefficient of variation (CV)0.61228498
Kurtosis-1.120763
Mean18.652378
Median Absolute Deviation (MAD)9.389354
Skewness-0.22403983
Sum43460.041
Variance130.42944
MonotonicityNot monotonic
2025-02-22T17:53:40.178475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.8745456 184
 
5.4%
5.135871 158
 
4.7%
21.0238262 126
 
3.7%
24.5192012 114
 
3.4%
24.8566214 113
 
3.3%
1.7883892 105
 
3.1%
30.4131802 90
 
2.7%
0.9718696 81
 
2.4%
24.0972706 78
 
2.3%
20.5509408 75
 
2.2%
Other values (87) 1206
35.5%
(Missing) 1065
31.4%
ValueCountFrequency (%)
0.8569106 3
 
0.1%
0.9718696 81
2.4%
1.1321908 11
 
0.3%
1.3751582 33
 
1.0%
1.472718 2
 
0.1%
1.7883892 105
3.1%
2.1494226 16
 
0.5%
2.3370854 6
 
0.2%
2.4955424 73
2.2%
2.5732174 1
 
< 0.1%
ValueCountFrequency (%)
43.0592916 40
 
1.2%
40.934725 1
 
< 0.1%
34.3118438 26
 
0.8%
34.282638 3
 
0.1%
33.7569336 4
 
0.1%
33.4698468 41
 
1.2%
33.0665582 2
 
0.1%
33.0187104 2
 
0.1%
32.8745456 184
5.4%
32.0648614 1
 
< 0.1%

userId
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57423953
Minimum10427670
Maximum98345808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.7 KiB
2025-02-22T17:53:40.371814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10427670
5-th percentile19555569
Q133295482
median49241808
Q381880524
95-th percentile98345808
Maximum98345808
Range87918138
Interquartile range (IQR)48585042

Descriptive statistics

Standard deviation26747715
Coefficient of variation (CV)0.46579369
Kurtosis-1.3374514
Mean57423953
Median Absolute Deviation (MAD)19931868
Skewness0.12049847
Sum1.9495432 × 1011
Variance7.1544027 × 1014
MonotonicityNot monotonic
2025-02-22T17:53:40.570637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98345808 192
 
5.7%
35897499 170
 
5.0%
81375624 160
 
4.7%
65023200 147
 
4.3%
32751774 130
 
3.8%
37412595 118
 
3.5%
97867440 114
 
3.4%
29309940 105
 
3.1%
88561539 102
 
3.0%
46009656 99
 
2.9%
Other values (75) 2058
60.6%
ValueCountFrequency (%)
10427670 11
 
0.3%
10909503 80
2.4%
11299464 27
 
0.8%
13066218 35
1.0%
14260257 3
 
0.1%
14996520 7
 
0.2%
17969193 1
 
< 0.1%
19555569 57
1.7%
24408549 42
1.2%
24478344 64
1.9%
ValueCountFrequency (%)
98345808 192
5.7%
97867440 114
3.4%
95980995 2
 
0.1%
95411349 18
 
0.5%
94947534 42
 
1.2%
93202560 38
 
1.1%
92911698 15
 
0.4%
92283246 62
 
1.8%
92192265 10
 
0.3%
90692118 91
2.7%

stationId
Real number (ℝ)

Distinct105
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean576789.68
Minimum129465
Maximum995505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.7 KiB
2025-02-22T17:53:40.758455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum129465
5-th percentile219054
Q1369001
median549414
Q3864630
95-th percentile955429
Maximum995505
Range866040
Interquartile range (IQR)495629

Descriptive statistics

Standard deviation257486.31
Coefficient of variation (CV)0.44641283
Kurtosis-1.2820935
Mean576789.68
Median Absolute Deviation (MAD)189486
Skewness0.14996358
Sum1.958201 × 109
Variance6.62992 × 1010
MonotonicityNot monotonic
2025-02-22T17:53:40.965705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
369001 334
 
9.8%
474204 213
 
6.3%
955429 190
 
5.6%
228137 104
 
3.1%
878706 91
 
2.7%
250527 89
 
2.6%
944515 83
 
2.4%
207262 80
 
2.4%
664306 69
 
2.0%
987396 67
 
2.0%
Other values (95) 2075
61.1%
ValueCountFrequency (%)
129465 35
 
1.0%
131897 23
 
0.7%
134427 4
 
0.1%
191826 13
 
0.4%
200695 13
 
0.4%
207262 80
2.4%
219054 64
1.9%
228137 104
3.1%
236379 23
 
0.7%
236840 4
 
0.1%
ValueCountFrequency (%)
995505 30
 
0.9%
989457 4
 
0.1%
988981 3
 
0.1%
987396 67
 
2.0%
981639 5
 
0.1%
955429 190
5.6%
951880 35
 
1.0%
946482 1
 
< 0.1%
944575 21
 
0.6%
944515 83
2.4%

locationId
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean629934.46
Minimum125372
Maximum978130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.7 KiB
2025-02-22T17:53:41.131365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum125372
5-th percentile144857
Q1481066
median503205
Q3878393
95-th percentile976902
Maximum978130
Range852758
Interquartile range (IQR)397327

Descriptive statistics

Standard deviation255620.99
Coefficient of variation (CV)0.40578982
Kurtosis-1.0124264
Mean629934.46
Median Absolute Deviation (MAD)145134
Skewness-0.072583959
Sum2.1386275 × 109
Variance6.5342092 × 1010
MonotonicityNot monotonic
2025-02-22T17:53:41.272171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
493904 524
15.4%
976902 401
11.8%
461655 393
11.6%
868085 294
8.7%
481066 276
8.1%
928191 235
 
6.9%
144857 181
 
5.3%
503205 168
 
4.9%
566549 158
 
4.7%
978130 125
 
3.7%
Other values (15) 640
18.9%
ValueCountFrequency (%)
125372 47
 
1.4%
144857 181
 
5.3%
202527 103
 
3.0%
310085 1
 
< 0.1%
399399 47
 
1.4%
454147 4
 
0.1%
461655 393
11.6%
481066 276
8.1%
493904 524
15.4%
503205 168
 
4.9%
ValueCountFrequency (%)
978130 125
 
3.7%
976902 401
11.8%
948590 76
 
2.2%
928191 235
6.9%
878393 20
 
0.6%
868085 294
8.7%
814002 108
 
3.2%
751082 32
 
0.9%
747048 48
 
1.4%
700367 4
 
0.1%

managerVehicle
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
1
2022 
0
1373 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3395
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 2022
59.6%
0 1373
40.4%

Length

2025-02-22T17:53:41.423643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T17:53:41.518881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2022
59.6%
0 1373
40.4%

Most occurring characters

ValueCountFrequency (%)
1 2022
59.6%
0 1373
40.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2022
59.6%
0 1373
40.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2022
59.6%
0 1373
40.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2022
59.6%
0 1373
40.4%

facilityType
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
3
1832 
2
862 
1
593 
4
 
108

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3395
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 1832
54.0%
2 862
25.4%
1 593
 
17.5%
4 108
 
3.2%

Length

2025-02-22T17:53:41.633534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T17:53:41.735255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 1832
54.0%
2 862
25.4%
1 593
 
17.5%
4 108
 
3.2%

Most occurring characters

ValueCountFrequency (%)
3 1832
54.0%
2 862
25.4%
1 593
 
17.5%
4 108
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1832
54.0%
2 862
25.4%
1 593
 
17.5%
4 108
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1832
54.0%
2 862
25.4%
1 593
 
17.5%
4 108
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1832
54.0%
2 862
25.4%
1 593
 
17.5%
4 108
 
3.2%

Mon
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
0
2779 
1
616 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3395
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2779
81.9%
1 616
 
18.1%

Length

2025-02-22T17:53:41.866710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T17:53:41.964718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2779
81.9%
1 616
 
18.1%

Most occurring characters

ValueCountFrequency (%)
0 2779
81.9%
1 616
 
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2779
81.9%
1 616
 
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2779
81.9%
1 616
 
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2779
81.9%
1 616
 
18.1%

Tues
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
0
2760 
1
635 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3395
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2760
81.3%
1 635
 
18.7%

Length

2025-02-22T17:53:42.082522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T17:53:42.172838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2760
81.3%
1 635
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 2760
81.3%
1 635
 
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2760
81.3%
1 635
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2760
81.3%
1 635
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2760
81.3%
1 635
 
18.7%

Wed
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
0
2682 
1
713 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3395
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2682
79.0%
1 713
 
21.0%

Length

2025-02-22T17:53:42.285992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T17:53:42.380277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2682
79.0%
1 713
 
21.0%

Most occurring characters

ValueCountFrequency (%)
0 2682
79.0%
1 713
 
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2682
79.0%
1 713
 
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2682
79.0%
1 713
 
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2682
79.0%
1 713
 
21.0%

Thurs
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
0
2660 
1
735 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3395
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2660
78.4%
1 735
 
21.6%

Length

2025-02-22T17:53:42.516990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T17:53:42.610909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2660
78.4%
1 735
 
21.6%

Most occurring characters

ValueCountFrequency (%)
0 2660
78.4%
1 735
 
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2660
78.4%
1 735
 
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2660
78.4%
1 735
 
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2660
78.4%
1 735
 
21.6%

Fri
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
0
2785 
1
610 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3395
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2785
82.0%
1 610
 
18.0%

Length

2025-02-22T17:53:42.724420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T17:53:42.813825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 2785
82.0%
1 610
 
18.0%

Most occurring characters

ValueCountFrequency (%)
0 2785
82.0%
1 610
 
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2785
82.0%
1 610
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2785
82.0%
1 610
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2785
82.0%
1 610
 
18.0%

Sat
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
0
3333 
1
 
62

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3395
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3333
98.2%
1 62
 
1.8%

Length

2025-02-22T17:53:42.925653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T17:53:43.038876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3333
98.2%
1 62
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 3333
98.2%
1 62
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3333
98.2%
1 62
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3333
98.2%
1 62
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3333
98.2%
1 62
 
1.8%

Sun
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
0
3371 
1
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3395
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3371
99.3%
1 24
 
0.7%

Length

2025-02-22T17:53:43.148650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T17:53:43.243300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3371
99.3%
1 24
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 3371
99.3%
1 24
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3371
99.3%
1 24
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3371
99.3%
1 24
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3371
99.3%
1 24
 
0.7%

reportedZip
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.7 KiB
1
2390 
0
1005 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3395
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 2390
70.4%
0 1005
29.6%

Length

2025-02-22T17:53:43.352258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-22T17:53:43.444119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 2390
70.4%
0 1005
29.6%

Most occurring characters

ValueCountFrequency (%)
1 2390
70.4%
0 1005
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 2390
70.4%
0 1005
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 2390
70.4%
0 1005
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 2390
70.4%
0 1005
29.6%

Interactions

2025-02-22T17:53:32.238387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:17.052400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:18.652370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:20.137734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:21.796223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:23.767329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:26.005508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:27.553322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:29.216857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:30.733153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:32.406586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:17.199865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:18.800571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:20.294055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:21.926934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:24.024882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:26.149541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:27.695917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:29.351381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:30.874044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:32.556472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:17.342564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:18.952321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:20.483753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:22.067759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:24.244669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:26.311427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:27.846096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:29.566935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:31.013625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:32.722429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:17.503341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:19.106531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:20.655058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:22.230916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:24.510631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:26.504909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:28.000049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:29.725643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:31.192134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:32.859439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:17.638561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:19.236457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:20.822402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:22.553337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:24.753973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:26.643669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:28.143980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:29.857689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:31.350303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:32.998302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:17.767090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:19.381190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:20.964439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:22.679834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:24.950676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:26.783621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:28.275807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:29.992847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:31.504130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:33.156747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:17.909688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:19.547413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:21.137494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:22.855083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:25.212830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:26.917279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:28.457066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:30.132537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:31.666521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:33.306616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:18.206300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:19.705167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:21.288567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:23.142486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:25.410143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:27.061402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:28.598671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:30.269361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:31.812027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:33.510794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:18.360710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:19.838708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:21.497551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:23.381228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:25.636231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:27.195976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:28.943916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:30.426221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:31.948124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:33.685994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:18.515904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:20.003734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:21.647201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:23.552361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:25.805190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:27.344930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:29.077402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:30.589209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-22T17:53:32.088372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-22T17:53:43.568528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
FriMonSatSunThursTuesWedchargeTimeHrsdistancedollarsendTimefacilityTypekwhTotallocationIdmanagerVehicleplatformreportedZipsessionIdstartTimestationIduserIdweekday
Fri1.0000.2190.0590.0300.2450.2230.2400.0000.0000.0000.0260.0210.0000.0000.0030.0000.0000.0650.0000.0000.0190.999
Mon0.2191.0000.0590.0310.2460.2240.2410.0230.0000.0000.0000.0130.0380.0000.0000.0000.0000.0000.0000.0400.0530.999
Sat0.0590.0591.0000.0000.0670.0600.0650.0000.1690.0000.1470.2370.1460.1170.0240.0560.0440.0120.1850.1410.1390.999
Sun0.0300.0310.0001.0000.0360.0320.0350.0000.1690.0000.4230.1620.1060.0690.0680.0000.0000.0000.3890.0810.1480.999
Thurs0.2450.2460.0670.0361.0000.2510.2700.0060.0110.0130.0000.0350.0000.0540.0370.0000.0000.0000.0060.0000.0550.999
Tues0.2230.2240.0600.0320.2511.0000.2460.0000.0670.0000.0000.0000.0300.0470.0000.0000.0000.0000.0000.0000.0000.999
Wed0.2400.2410.0650.0350.2700.2461.0000.0320.0000.0470.0000.0160.0000.0410.0000.0000.0000.0270.0000.0380.0000.999
chargeTimeHrs0.0000.0230.0000.0000.0060.0000.0321.0000.0980.5460.2370.0600.4540.0350.0000.0000.014-0.008-0.0960.0340.0190.000
distance0.0000.0000.1690.1690.0110.0670.0000.0981.0000.034-0.1950.4620.327-0.1410.5190.3351.000-0.000-0.2320.0070.3550.098
dollars0.0000.0000.0000.0000.0130.0000.0470.5460.0341.0000.1780.0480.1260.0800.0460.0000.057-0.014-0.0540.0120.0230.000
endTime0.0260.0000.1470.4230.0000.0000.0000.237-0.1950.1781.0000.258-0.0550.1160.2430.1120.2610.0040.8890.0410.0490.182
facilityType0.0210.0130.2370.1620.0350.0000.0160.0600.4620.0480.2581.0000.1380.5220.3130.1310.3720.0000.2600.4300.3740.166
kwhTotal0.0000.0380.1460.1060.0000.0300.0000.4540.3270.126-0.0550.1381.000-0.0020.3200.0810.223-0.025-0.2110.0130.0900.074
locationId0.0000.0000.1170.0690.0540.0470.0410.035-0.1410.0800.1160.522-0.0021.0000.3130.2480.444-0.0080.097-0.0580.0760.061
managerVehicle0.0030.0000.0240.0680.0370.0000.0000.0000.5190.0460.2430.3130.3200.3131.0000.0330.0340.0000.2570.2630.5720.081
platform0.0000.0000.0560.0000.0000.0000.0000.0000.3350.0000.1120.1310.0810.2480.0331.0000.4720.0220.1170.1380.2910.019
reportedZip0.0000.0000.0440.0000.0000.0000.0000.0141.0000.0570.2610.3720.2230.4440.0340.4721.0000.0340.2620.2290.3910.036
sessionId0.0650.0000.0120.0000.0000.0000.027-0.008-0.000-0.0140.0040.000-0.025-0.0080.0000.0220.0341.000-0.0100.0170.0090.017
startTime0.0000.0000.1850.3890.0060.0000.000-0.096-0.232-0.0540.8890.260-0.2110.0970.2570.1170.262-0.0101.0000.0410.0530.174
stationId0.0000.0400.1410.0810.0000.0000.0380.0340.0070.0120.0410.4300.013-0.0580.2630.1380.2290.0170.0411.0000.0050.068
userId0.0190.0530.1390.1480.0550.0000.0000.0190.3550.0230.0490.3740.0900.0760.5720.2910.3910.0090.0530.0051.0000.086
weekday0.9990.9990.9990.9990.9990.9990.9990.0000.0980.0000.1820.1660.0740.0610.0810.0190.0360.0170.1740.0680.0861.000

Missing values

2025-02-22T17:53:33.946937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-22T17:53:34.240283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

sessionIdkwhTotaldollarscreatedendedstartTimeendTimechargeTimeHrsweekdayplatformdistanceuserIdstationIdlocationIdmanagerVehiclefacilityTypeMonTuesWedThursFriSatSunreportedZip
013665637.780.000014-11-18 15:40:260014-11-18 17:11:0415171.510556TueandroidNaN358974995828734616550301000000
130757239.740.000014-11-19 17:40:260014-11-19 19:51:0417192.177222WedandroidNaN358974995494144616550300100000
242287886.760.580014-11-21 12:05:460014-11-21 16:46:0412164.671667FriandroidNaN358974991294654616550300001000
331732846.170.000014-12-03 19:16:120014-12-03 21:02:1819211.768333WedandroidNaN358974995698894616550300100000
432665000.930.000014-12-11 20:56:110014-12-11 21:14:0620210.298611ThuandroidNaN358974994140885665490300010000
540993662.140.000014-12-12 14:38:440014-12-12 15:04:0414150.422222FriandroidNaN358974999112312025270300001000
650842440.300.000014-12-12 15:08:400014-12-12 15:47:0415150.640000FriandroidNaN358974999202644616550300001000
729484361.820.000014-12-17 20:30:250014-12-17 21:31:0420211.010833WedandroidNaN358974994317964616550300100000
835159130.810.000014-12-18 17:53:190014-12-18 18:04:0417180.179167ThuandroidNaN358974991344276209060300010000
984900141.980.000014-12-18 18:06:490014-12-18 18:30:0518180.387778ThuandroidNaN358974992072629281910300010000
sessionIdkwhTotaldollarscreatedendedstartTimeendTimechargeTimeHrsweekdayplatformdistanceuserIdstationIdlocationIdmanagerVehiclefacilityTypeMonTuesWedThursFriSatSunreportedZip
338585873810.020.00015-09-23 16:44:240015-09-23 17:11:1416170.447222Wedios13.352643595747355875146483390200100001
338635103630.080.00015-09-23 19:16:390015-09-23 20:58:0719201.691111Wedios13.352643595747355402256483390200100001
338791455450.060.00015-09-24 18:49:030015-09-24 20:09:0618201.334167Thuios13.352643595747355402256483390200010001
338879483116.730.00015-09-29 16:37:200015-09-29 20:05:1116203.464167Tueios13.352643595747358847076483390201000001
338980455736.560.00015-09-30 16:59:040015-09-30 20:27:0616203.467222Wedios13.352643595747355402256483390200100001
339070215656.740.00015-10-01 16:31:180015-10-01 19:59:0816193.463889Thuios13.352643595747358847076483390200010001
339137580926.860.00015-10-02 16:28:480015-10-02 19:27:0516192.971389Friios13.352643595747358847076483390200001001
339258583746.070.00015-09-30 16:54:220015-09-30 20:24:0616203.495556Wedandroid2.337085320708526385368680850300100001
339325866455.740.00015-09-24 11:43:020015-09-24 13:55:1211132.202778Thuios4.671064580232078182177003671200010001
339478606086.950.00015-10-01 16:43:050015-10-01 19:42:0616192.983611Thuios3.308334260988756643068680850300010001